1.最短路(Floyd、Dijstra,BellmanFord) 2.最小生成树(先写个prim,kruscal要用并查集,不好写) 3.大数(高精度)加减乘除 4.二分查找. (代码可在五行以内) 5.叉乘、判线段相交、然后写个凸包. 6.BFS、DFS,同时熟练hash表(要熟,要灵活,代码要简) 7.数学上的有:辗转相除(两行内),线段交点、多角形面积公式...
It's John Sonmez from simpleprogrammer.com. I have a question here that a lot of people have been asking lately since I did this video on “Do I need to learn algorithms?” Now, the question is “How to learn algorithms?” Obviously, that was—I must have known that was coming. Thi...
Because of this discovery, it is possible than even faster algorithms will be discovered. It is therefore natural to ask: did fast human calculators of the past use faster algorithms – in which case we can learn from their experience – or they simply performed all operations within a ...
Here are some things to learn that will help you build a strong foundation: Programming languages. Algorithm developers use programming languages to create algorithms. Some common languages used are Python, Java, R, and JavaScript. Learn the types of algorithms developed in each of these languages...
We don’t know what the function (f) looks like or it’s form. If we did, we would use it directly and we would not need to learn it from data using machine learning algorithms. It is harder than you think. There is also error (e) that is independent of the input data (X). ...
You can’t use machine learning unless you know how to program. Luckily, we have a free guide:How to Learn Python for Data Science, The Self-Starter Way Statistics for Data Science Statistics, especially Bayesian probability, underpins many ML algorithms. We have a free guide:How to Learn ...
Algorithms now determine how much things cost. It’s called dynamic pricing and it adjusts according to current market conditions in order to increase profits. The rise of e-commerce has propelled pricing algorithms into an everyday occurrence—whether y
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These indicators are indispensable to complete advanced algorithms, and these indicators can be achieved through some classic algorithms . This is why I always emphasize that must first learn the basic data structure and algorithm . However, it is not easy to say that these narrations are complete...
In supervised learning, training means using historical data to build a machine learning model that minimizes errors. The number of minutes or hours necessary to train a model varies a great deal between algorithms. Training time is often closely tied to accuracy; one typically accompanies the othe...